In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system that provides high performance with low latency.
In this work, to promote the word-level representation learning in end-to-end ASR, we propose a hierarchical conditional model that is based on connectionist temporal classification (CTC).
Recognition of the mental state of a human character in text is a major challenge in natural language processing.
As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased.
While Mask-CTC achieves remarkably fast inference speed, its recognition performance falls behind that of conventional autoregressive (AR) systems.
In this work, Mask CTC model is trained using a Transformer encoder-decoder with joint training of mask prediction and CTC.
Audio and Speech Processing Sound
Human semantic knowledge about concepts acquired through perceptual inputs and daily experiences can be expressed as a bundle of attributes.
However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions.
This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words.